a ggplot2 grammar guide

These are my ideas about the grammar of ggplot2. ggplot2 is based on the grammar of graphics. But it has its own grammar too — it is a particular implementation of the grammar of graphics — and a lot of really helpful decisions are part of its grammar (like having set defaults for aesthetic scales, and scale and plot labels make it easier to build plots quickly). If you are teaching ggplot2, you might think about teaching the grammar of ggplot2 first and then come back to the grammar of graphics — because there will be more context for seeing how an implemenation relates to the larger philosophy. There are a lot of great resources out there already teaching the grammar of graphics which are sources of inspiration for the guide.

Not into human language grammar? Just ignor that first column!

	What?		How?
1. The Declarative Mood	Declaring the data		ggplot(data = gapminder) +
2. The Interogative Mood	Asking for representation of variables by aesthetics (color, size, x position, etc.) (also known as aesthetic mapping)		aes(color = pop) +
- Modifiers I	modifying default aesthetic labels (and plot labels)		labs(color = continent) + labs(title = “my title”) +
- Modifiers II	modifying the default coordinate system (how the positional aesthetics appear - x and y)		coord_polar() +
- Modifiers III	modifying default aesthetic scales		scale_color_viridis_d() +
3. Nouns	geometric layers taking on the aesthetics representing variables	see below table	geom_point() +
4.The Conditional Mood	Making local, geom-specific declarations rather than global declarations		geom_point(
	data is geom-specific		data = gapminder,
	aesthetic representation is geom-specific		aes(size = population),
	aesthetics not representing variables; unmapped aesthetics (i.e. The Imperative Mood)		color = “blue”
			) +
5. Interjections	Adding context with annotation layers		annotate(geom = “point”, x = 10, y = 12, color = “blue”)
6. Punctuation	faceting breaks a plot into small plots (or “small multiples”) based on categorical variables		facet_wrap(~continent)
7. Greetings	themes changing plot look and feel		theme_minimal()
8. The Written Language	save plots with different resolutions and file formats		ggsave(file = “plot.png”, plot = g, height = “4in”, width = “6in”)
9. Composition	Composing plots into ensembles		library(patchwork) (g1 + g2) | g3 library(cowplot) plotgrid(…,)
10. Concision	We’ve been intentionally verbose, but there are strategies for being concise		last_plot(), switch out data with %+%, writing functions

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A closer look at geoms.

Above, the geom topics are intentionally cursory. There are tons of geoms, and they can get distracting. But in the end, geoms are pretty fun - and there are a lot of them! Below gives you a preview of geometric marks used to describe continuous distributions, continuous-continuous joint distributions, visualizations of single series, of multiple series and discrete-discrete distributions.

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A Closer Look at Color

• color theory
• named colors in R
• paletteer’s large collection of palettes

A Closer Look at Text

• font? someday
• {ggtext} allows variation in color, font type, etc within text

External Theme Packages

• {ggeasy} easy adjustment for common theme modifications
• External Themes, {ggthemes} {ggthemr}

Spatial with geom_sf and tmap

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Network visualization {ggraph} and {tidygraph}

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Animation with {gganimate}

Other guides to ggplot2

ggplot2 is very flexible, so there often there are more than one way of getting things done — which also means there is more than one way to introduce ggplot2. If you aren’t on board with this guide (and even if you are) you may want check out other resouces — I’ve learned a great deal from them! The definitive and comprehensive guide to ggplot2 is the 3rd edition of ggplot2: elegant graphics for data analysis. Some other workshop materials are Alison Hill’s Take a Sad Plot and Make it Better, Garrick Aden-Buie’s A Gentle Guide to the Grammar of Graphics with ggplot2, Will Chase’s R you ready to make charts?, Malcom Barrett’s Designing ggplots making clear figures that communicate, and Thomas Lin Pederson’s Drawing anything with ggplot2. The ggplot2 cheatsheet is also excellent.

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Other Chapters

Data Wrangling Statistical Analysis